Geometric modeling and processing

This work aims to analyze the characteristics and importance that design techniques for disassembly assume in the modern design phase of a mechanism. To this end, the study begins by considering a three-dimensional model of a gear motor, taken from the components of which the overall drawings are arranged and from the relief of those not available. Once the mechanism has been digitally reconstructed, the activity focuses on the study of the optimal disassembly sequence by comparing different methodologies, according to two evaluation criteria-minimizing the time taken and minimizing the number of tool changes necessary to complete the sequence. The main results of the work are (1) defining a standard methodology to improve disassembly sequence planning, (2) finding the best disassembly sequence for the specific component among the literature and eventually new methods, and (3) offering to the industrial world a way to optimize maintenance operations in mechanical products. Referring to the limitation of the present works, it can be affirmed that the results are limited to the literature explored and to the case study examined.

Keywords: CAD , Disassembly sequence planning , Globoid gear motor

In the region of the cultural patrimony, operators use high-resolution orthophotos of paintings for the restoration, monitoring and electronic recording and exhibition purposes. Unfortunately, artworks that are to be restored and/or shown in digital museums are painted on canvas that are far from perfectly planar. Professional documents surrounding an artwork to be preserved in digital collections or museums can therefore be enhanced with information relating to the paintings' 3D structure. This paper proposes both the design of a portable low-cost device that enables the acquisition of 3D geometry of painting and the procedure for triangulation of 3D data. This process uses a set of fiducial markers to set and continuously control the mutual orientation of the laser source and the camera and works accordingly to the principle of laser-camera triangulation.

Keywords: 3D Laser Scanner , Cultural Heritage , Marker Detection , Pose Estimation , Reverse Engineering

In brain tumor surgery, an appropriate and careful surgical planning process is crucial for surgeons and can determine the success or failure of the surgery. A deep comprehension of spatial relationships between tumor borders and surrounding healthy tissues enables accurate surgical planning that leads to the identification of the optimal and patient-specific surgical strategy. A physical replica of the region of interest is a valuable aid for preoperative planning and simulation, allowing the physician to directly handle the patient's anatomy and easily study the volumes involved in the surgery. In the literature, different anatomical models, produced with 3D technologies, are reported and several methodologies were proposed. Many of them share the idea that the employment of 3D printing technologies to produce anatomical models can be introduced into standard clinical practice since 3D printing is now considered to be a mature technology. Therefore, the main aim of the paper is to take into account the literature best practices and to describe the current workflow and methodology used to standardize the pre-operative virtual and physical simulation in neurosurgery. The main aim is also to introduce these practices and standards to neurosurgeons and clinical engineers interested in learning and implementing cost-effective in-house preoperative surgical planning processes. To assess the validity of the proposed scheme, four clinical cases of preoperative planning of brain cancer surgery are reported and discussed. Our preliminary results showed that the proposed methodology can be applied effectively in the neurosurgical clinical practice both in terms of affordability and in terms of simulation realism and efficacy.

Keywords: 3D casting , 3D printing , Additive manufacturing , Brain , Cancer , Computer aided design , Neurosurgery , Physical simulation , Preoperative planning , Virtual planning

Autologous ear reconstruction, i.e. the reconstruction of the outer ear from autologous cartilage tissue, is a very important surgery considering the psychosocial repercussions of an individual affected by microtia (i.e. the total or partial absence of the outer ear). The execution of this surgery can be very complex due to the unique characteristics of this anatomical region. In order to help the surgeon in the process of cutting and suturing, innovative surgical guides were designed that can transmit information about the shape and size of the anatomy to be reconstructed. This work lays the foundation for the creation of a semi-automatic and easy-to-use tool for the modeling of surgical guides. The goal is to make the hospital staff autonomous in the creation of instruments that can be used in pre-surgical simulation and during surgery.

Keywords: Autologous Ear Reconstruction , Bioengineering , Computer Aided Design , Medical Devices and Equipment , Microtia

The most common clinical treatment for ear deformities or non-congenital abnormalities is the reconstruction of the missing geometry using autologous costal cartilage. The surgical procedure consists in cutting, sculpting and suturing harvested costal cartilage from the patient to recreate an ear shape which is symmetric to the contralateral ear. During chirurgical operation, surgeons needs an accurate 3D template as reference to reproduce the ear. For this purpose, reverse engineering and additive manufacturing techniques can be employed. Specifically, this works aims to develop a reliable, low-cost and user-friendly system, to acquire the healthy ear geometry in clinical environment avoiding head patient's exposition to radiation (MRI, CT scan). An ideal acquisition setup and device have been selected to achieve accurate results. To this end, a casted model of an ear was created as reference, and the best setup was evaluated by comparing the obtained 3D reconstructions with it. Once the setup has been determined, the anatomies of five volunteers were acquired, to test the methodology on human subjects.

Keywords: 3D model , Ear , Microtia , Photogrammetry , RealSense D415

In cranioplasty surgery, achieving an effective aesthetic shape restoration of the cranial vault is the most important issue to ensure a proper outcome in terms of social and psychological benefits for the patient. To date, the most advanced approach uses CT/MRI data to reconstruct, in a pre-operative stage, the 3D anatomy of the defective skull in order to design a patient-specific prosthesis. In the last years, several techniques have been proposed to improve the applicability of such approach in the clinical practice, but the analysis of the related literature shows still open issues, due to the wide anatomical variability and complexity of the craniofacial anatomy that needs to be retrieved. With the aim to overcome the State-of-the-Art drawbacks, a new semi-automatic hybrid procedure for repairing unilateral or quasi-unilateral (i.e. a single defect slightly passing the sagittal plane) cranial defects is presented. The novel approach is hybrid because a surface interpolation for filling the hole is used together with a template-based reconstruction guided by the healthy counterpart. The procedure, being landmark-independent and avoiding any patch adaptation, represents a valid alternative for the existing approaches also in terms of user's burden, requiring less time consuming and less cumbersome operations. In addition, a new evaluating technique able to measure the symmetry of the reconstruction as well as the continuity between patch and healthy bone is proposed to test the procedure performance. Several test cases have been then addressed to prove the effectiveness and repeatability of the proposed procedure in reconstructing large-size defects of the skull.

Keywords: CAD , Cranioplasty , Reverse Engineering , Skull Reconstruction

The paper aims at providing an overview on the current automation level of geometric verification process with reference to some aspects that can be considered crucial to achieve a greater efficiency, accuracy and repeatability of the inspection process. Although we are still far from making this process completely automatic, several researches were made in recent years to support and speed up the geometric error evaluation and to make it less human-intensive. The paper, in particular, surveys: (1) models of specification developed for an integrated approach to tolerancing; (2) state of the art of Computer-Aided Inspection Planning (CAIP); (3) research efforts recently made for limiting or eliminating the human contribution during the data processing aimed at geometric error evaluation. Possible future perspectives of the research on the automation of geometric verification process are finally described.

Keywords: Automatic geometric verification , Computer-Aided Inspection Planning , Error evaluation , Feature Recognition , GPS standards , Model of specification for tolerancing , Partition

Background: Sleep-Related Breathing Disorders are characterized by repeated episodes of complete or partial obstruction of the upper airway during sleep. Mandibular advancement devices represent a non-invasive treatment in reducing the number of respiratory events and in decreasing symptoms. The advancement extent of these devices is responsible for the mandibular roto-translation and its effects on the temporomandibular joint. Methods: This study defined a systematic method to assess the mandible roto translation that is caused by MADs according to a scan-to-CAD approach. Starting from a closed mouth position and simulating the oral appliance at different settings it was possible to define a local reference system that is useful for the evaluation of the mandibular roto-translation. This latter was then applied to evaluate the movements of the condyle and the mandibular dental arch. Results: MAD1 resulted in a reduced mouth opening and protrusion, while MAD2 enabled a higher degree of motion of the mandible useful for patients who need an important protrusion. Conclusions: The two devices present different dynamics. Results that are achievable employing this method can be directly used by practitioners in comparing MADs, as well as by researchers in evaluating MADs effects.

Keywords: Computer-aided design , Digital dentistry , Digital workflow , Mandibular advancement device , Obstructive sleep apnea syndrome

Continuous innovation in the field of high-end motor vehicle chassis demands optimization of the weight/stiffness ratio and to achieve high quality standards. The use of light materials, such as aluminum alloys, is therefore increasingly common in the design of the chassis, whose assembly process represents a technological challenge. Welding joining processes, and in particular robot-based welding, are widely used in automotive field despite causing distortions. To predict these deformations, finite element analyzes are performed, in particular thermo-elasto-plastic simulations, which are able to satisfactorily replicate the behavior of residual stresses and strains after cooling. However, such analyzes are computationally expensive making their application difficult to complex structures. This work would investigate an alternative solution to predict distortions that effectively returns the behavior of welded assemblies. A CAE-based model for TEP analysis of welded joints is proposed. As a case study, the T-welded junction between two aluminum alloy plates (T-Joint) was considered. The model is validated by a preliminary experimental campaign.

Keywords: CAE-based model , TEP analysis , Termo-elasto-plastic model , Welding process

This work investigates vibration-supported, force-controlled fine machining with elastic bonded mounted points for automated fine processing of mould steel samples. The aim is to compare conventional robot- or machine-tool-based face grinding with a vibration-supported grinding process. The influence of vibration support on the surface topography is investigated primarily to minimize kinematically caused grinding traces. First, the state of the art for the production of tool moulds and vibration-supported fine machining is explained. On this basis, the potentials for the reduction of grinding marks through vibration support for an increase in the degree of automation are derived and the experimental procedure is introduced. Subsequently, robot-based grinding tests with vibration support are carried out and compared with conventional grinding tests. After the tests carried out, the results are evaluated using tactile and optical measuring methods.

Keywords: Finishing , Grinding , Moulds , Vibration-assisted

This work proposes an innovative method for evaluating usersâ™ engagement, combining the User Engagement Scale (UES) questionnaire and a facial expression recognition (FER) system, active research topics of increasing interest in the humanâ"computer interaction domain (HCI). The subject of the study is a 3D simulator that reproduces a virtual FabLab in which users can approach and learn 3D modeling software and 3D printing. During the interaction with the virtual environment, a structured-light camera acquires the face of the participant in real-time, to catch its spontaneous reactions and compare them with the answers to the UES closed-ended questions. FER methods allow overcoming some intrinsic limits in the adoption of questioning methods, such as the non-sincerity of the interviewees and the lack of correspondence with facial expressions and body language. A convolutional neural network (CNN) has been trained on the Bosphorus database (DB) to perform expression recognition and the classification of the video frames in three classes of engagement (deactivation, average activation, and activation) according to the model of emotion developed by Russell. The results show that the two methodologies can be integrated to evaluate user engagement, to combine weighted answers and spontaneous reactions and to increase knowledge for the design of the new product or service.

Keywords: 3D simulator , CNN , Deep learning , Facial expression recognition , Human-computer interaction , User engagement scale , User-centered design

The pre-concept design of the DEMO Vacuum Vessel is going on in view of the 2020 gate review; moreover the nuclear heat loads on the vessel inner shell were determined and found to be about one order of magnitude higher compared to ITER. A subsequent thermal-structural analysis of the vessel inner shell revealed high thermal stresses and a large temperature gradient through the inner shell thickness. Given the simultaneous occurrence of primary membrane stresses in the entire vessel inboard wall and, in proximity of the vessel ribs, high bending stresses due to the coolant pressure, a survey of all options within the design rules was required to identify the inter-dependencies of the individual stress limits (primary membrane, primary bending, thermal membrane plus bending). In order to face this kind of issues a detailed assessment on the design of the inboard wall of DEMO Vacuum Vessel has been conducted and is presented here. The current work evaluates both P and S type damages for the inboard wall of DEMO Vacuum Vessel in case of high nuclear heat load, vacuum vessel coolant pressure and toroidal field coil fast discharge. The elastic analysis method has been used to check the rules for prevention of both types of damage. The rules applied to prevent the aforementioned damages are compliant to Level A criteria, in case of negligible creep and negligible irradiation. In order to check the structural integrity of the inboard wall of DEMO VV against high thermal and mechanical loads, optimization structural analyses were performed and checked against the rules provided in the applicable design code (RCC MRx).

Keywords: Breeding blanket , CAD , DEMO , FEM , Ratcheting , Vacuum vessel

background: resin-bonded fixed dental prosthesis (RBFDP) represents a highly aesthetic and conservative treatment option to replace a single tooth in a younger patient. The purpose of this in vitro study was to compare the fracture strength and the different types of failure on anterior cantilever RBFDPs fabricated using zirconia (ZR), lithium disilicate (LD), and PMMA-based material with ceramic fillers (PM) by the same standard tessellation language (STL) file. Methods: sixty extracted bovine mandibular incisives were embedded resin block; scanned to design one master model of RBFDP with a cantilevered single-retainer. Twenty cantilevered single-retainer RBFDPs were fabricated using ZR; LD; and PM. Static loading was performed using a universal testing machine. Results: the mean fracture strength for the RBFDPs was: 292.5 Newton (Standard Deviation (SD) 36.6) for ZR; 210 N (SD 37.6) for LD; and 133 N (SD 16.3) for PM. All the failures of RBFDPs in ZR were a fracture of the abutment tooth; instead; the 80% of failures of RBFDPs in LD and PM were a fracture of the connector. Conclusion: within the limitations of this in vitro study, we can conclude that the zirconia RBFDPs presented load resistance higher than the maximum anterior bite force reported in literature (270 N) and failure type analysis showed some trends among the groups.

Keywords: Adhesive restorations , CAD/CAM , Digital dentistry , Fracture , Lithium disilicate , PMMA , Resin bonded bridge , Zirconia

In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet been defined. We aim to analyze and evaluate a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy. Each study is summarized and evaluated using a radar graph with six values: area under the curve (AUC), test accuracy, sensitivity, specificity, dataset size and labelling reliability. Plus, we defined the key points which should be taken into account when trying to accomplish this purpose and we compared each study with our baseline. In recent years, deep learning and, in particular, the convolution neural network (CNN), has achieved results comparable to those of humans in bone fracture classification. Adopting a correct generalization, we are reasonably sure that a computer-aided diagnosis (CAD) system, correctly designed to assist doctors, would save a considerable amount of time and would limit the number of wrong diagnoses.

Keywords: Bone fracture , CAD system , Deep learning , Neural network , Orthopedics , X-ray

The optimization of the aeronautical assembly lines is one of the most challenging tasks in development of a new aircraft. To date the Aeronautical companies are founding project related to the optimization of the manufacturing processes, impacting on the reduction of the non-recurring costs. The work presented focuses on methods for the implementation of the determinate assembly approach in the case of manufacturing of aeronautical structures. The methods are based on the prediction of the coupling tolerances between airframe components in order to reduce the numbers of the features to be manufactured in assembly. All that to lead to a reduction of the non-recurring costs.

Keywords: Aeronautical products , Assembly process , CAD , Determinant Assembly , Determinate Assembly , Hole to hole , Statistical distribution , Tolerance prediction

Nowadays, robots are heavily used in factories for different tasks, most of them including grasping and manipulation of generic objects in unstructured scenarios. In order to better mimic a human operator involved in a grasping action, where he/she needs to identify the object and detect an optimal grasp by means of visual information, a widely adopted sensing solution is Artificial Vision. Nonetheless, state-of-art applications need long training and fine-tuning for manually build the object's model that is used at run-time during the normal operations, which reduce the overall operational throughput of the robotic system. To overcome such limits, the paper presents a framework based on Deep Convolutional Neural Networks (DCNN) to predict both single and multiple grasp poses for multiple objects all at once, using a single RGB image as input. Thanks to a novel loss function, our framework is trained in an end-to-end fashion and matches state-of-art accuracy with a substantially smaller architecture, which gives unprecedented real-time performances during experimental tests, and makes the application reliable for working on real robots. The system has been implemented using the ROS framework and tested on a Baxter collaborative robot.

Keywords: Collaborative robotics , Deep learning , Industry 4.0 , Vision-guided robotic grasping

A 3D sketching system is presented for transforming an ideation to an actual CAD model using Human Computer Interaction (HCI). The system uses a Leap motion controller to capture human gestures, and translated them by a recognition model. The pinch and pull gestures are used to represent actions for flexible manipulations on control points coordinates of NURBS surfaces. The continuous motion of the designers hands is converted to morphological/geometrical and mathematical values. Both cases of single and dual hands are developed, offering the designer a variety of resolution tools to convey their ideas and creativity.

Keywords: 3D sketching , Human Computer Interaction Design , Leap Motion controller , NURBS surfaces

Polygonal meshes have a significant role in computer graphics, design and manufacturing technology for surface representation and it is often required to reduce their complexity to save memory. An efficient algorithm for detail retaining mesh simplification is proposed; in particular, the method presented is an iterative edge contraction algorithm based on the work of Garland and Heckberts. The original algorithm is improved by enhancing the quadratic error metrics with a penalizing factor based on discrete Gaussian curvature, which is estimated efficiently through the Gauss-Bonnet theorem, to account for the presence of fine details during the edge decimation process. Experimental results show that this new algorithm helps preserve the visually salient features of the model without compromising performance.

Keywords: Computational Geometry , Discrete Curvature , Mesh Simplification

Introduction: Miniscrew-assisted rapid palatal expansion (MARPE) appliances utilize the skeletal anchorage to expand the maxilla. One type of MARPE device is the Maxillary Skeletal Expander (MSE), which presents four micro-implants with bicortical engagement of the palatal vault and nasal floor. MSE positioning is traditionally planned using dental stone models and 2D headfilms. This approach presents some critical issues, such as the inability to identify the MSE position relative to skeletal structures, and the potential risk of damaging anatomical structures. Methods: A novel methodology has been developed to plan MSE position using the digital model of dental arches and cone-beam computed tomography (CBCT). A virtual model of MSE appliance with the four micro-implants was created. After virtual planning, a positioning guide is virtually designed, 3D printed, and utilized to model and weld the MSE supporting arms to the molar bands. The expansion device is then cemented in the patient oral cavity and micro-implants inserted. A clinical case of a 12.9-year-old female patient presenting a Class III malocclusion with transverse and sagittal maxillary deficiency is reported. Results: The midpalatal suture was opened with a split of 3.06 mm and 2.8 mm at the anterior and posterior nasal spine, respectively. After facemask therapy, the sagittal skeletal relationship was improved, as shown by the increase in ANB, A-Na perpendicular and Wits cephalometric parameters, and the mandibular plane rotated 1.6° clockwise. Conclusion: The proposed digital methodology represents an advancement in the planning of MSE positioning, compared to the traditional approach. By evaluating the bone morphol-ogy of the palate and midface on patient CBCT, the placement of MSE is improved regarding the biomechanics of maxillary expansion and the bone thickness at micro-implants insertion sites. In the present case report, the digital planning was associated with a positive outcome of maxillary expansion and protraction in safety conditions.

Keywords: CBCT , CBDP , Cephalometrics-based digital planning , MARPE , Miniscrew-assisted rapid palatal expansion , MSE , TAD , Virtual planning , Workflow

Purpose: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doctors in diagnosis of bone fractures, following the hierarchical classification proposed by the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation and the Orthopaedic Trauma Association (OTA). Methods: 2453 manually annotated images of proximal femur were used for the classification in different fracture types (1133 Unbroken femur, 570 type A, 750 type B). Secondly, the A type fractures were further classified into the types A1, A2, A3. Two approaches were implemented: the first is a fine-tuned InceptionV3 convolutional neural network (CNN), used as a baseline for our own proposed approach; the second is a multistage architecture composed by successive CNNs in cascade, perfectly suited to the hierarchical structure of the AO/OTA classification. Gradient Class Activation Maps (Grad-CAM) where used to visualize the most relevant areas of the images for classification. The averaged ability of the CNN was measured with accuracy, area under receiver operating characteristics curve (AUC), recall, precision and F1-score. The averaged ability of the orthopedists with and without the help of the CNN was measured with accuracy and Cohen's Kappa coefficient. Results: We obtained an averaged accuracy of 0.86 (CI 0.84−0.88) for three classes classification and 0.81 (CI 0.79−0.82) for five classes classification. The average accuracy improvement of specialists was 14 % with and without the CAD (Computer Assisted Diagnosis) system. Conclusion: We showed the potential of using a CAD system based on CNN for improving diagnosis accuracy and for helping students with a lower level of expertise. We started our work with proximal femur fractures and we aim to extend it to all bone segments further in the future, in order to implement a tool that could be used in every-day hospital routine.

Keywords: Bone fracture , Convolutional neural network , Deep Learning , Orthopaedics , X-Ray